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The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space.
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Y. N. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio, “Identifying and attacking the saddle point problem in high-dimensional non-convex optimization,” in
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G. F. Montufar, R. Pascanu, K. Cho, and Y. Bengio, “On the number of linear regions of deep neural networks,” in
2014
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J. H. Metzen, T. Genewein, V. Fischer, and B. Bischoff, “On detecting adversarial perturbations,”
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N. Cohen and A. Shashua, “Convolutional rectifier networks as generalized tensor decompositions,” in
2016
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